System and method for generating training recommendations via automated interaction evaluation

Abstract

A system for evaluating agent performance in interactions and generating training recommendations for agents based on the evaluated agent performance may include a computing device; a memory; and a processor, the processor configured to: create a plurality of evaluation prompts for evaluating interaction data items of one or more interactions; generate evaluation results for the interaction data items using the plurality of evaluation prompts and machine learning; create training recommendation prompts for the evaluation results; and generate training recommendations from training categories using the training recommendation prompts and machine learning.

What claimed is: 
     
         1 . A method of evaluating agent performance in interactions and generating training recommendations for agents based on the evaluated agent performance, the method comprising:
 creating a plurality of evaluation prompts for evaluating interaction data items of one or more interactions;   generating evaluation results for the interaction data items using the plurality of evaluation prompts and machine learning;   creating training recommendation prompts for the evaluation results; and   generating training recommendations from training categories using the training recommendation prompts and machine learning.   
     
     
         2 . A method according to  claim 1 , wherein generating evaluation results comprises comparing one or more performance indicators identified in the interaction data items with threshold values for the performance indicators. 
     
     
         3 . A method according to  claim 1 , wherein the training recommendation prompts are created for the evaluation results that comprise one or more performance indicators that are lower than thresholds for the one or more performance indicators. 
     
     
         4 . A method according to  claim 1 , wherein creating evaluation prompts comprises creating a vector store for the interaction data items. 
     
     
         5 . A method according to  claim 4 , wherein generating evaluation results for the evaluation prompts comprises conducting a similarity search using the vector store. 
     
     
         6 . A method according to  claim 1 , wherein the training categories are separated into training data items that are stored in a vector store. 
     
     
         7 . A method according to  claim 6 , wherein generating training recommendations comprises conducting a similarity search using the training data items vector store. 
     
     
         8 . A method according to  claim 1 , wherein the machine learning comprises generative artificial intelligence. 
     
     
         9 . A method according to  claim 8 , wherein the evaluation prompts and training recommendation prompts are submitted to a large language machine learning model. 
     
     
         10 . A method according to  claim 1 , wherein the training recommendations are based on previously generated evaluation results for the agent. 
     
     
         11 . A method according to  claim 1 , wherein each evaluation prompt of the plurality of evaluation prompts comprises interaction data items which are present in previously or subsequently created evaluation prompts. 
     
     
         12 . A system for evaluating agent performance in interactions and generating training recommendations for agents based on the evaluated agent performance, the system comprising:
 a computing device;   a memory; and   a processor, the processor configured to:
 create a plurality of evaluation prompts for evaluating interaction data items of one or more interactions; 
 generate evaluation results for the interaction data items using the plurality of evaluation prompts and machine learning; 
 create training recommendation prompts for the evaluation results; and 
 generate training recommendations from training categories using the training recommendation prompts and machine learning. 
   
     
     
         13 . A system according to  claim 12 , wherein the generation of evaluation results comprises comparing one or more performance indicators identified in the interaction data items with pre-set threshold values for the performance indicators. 
     
     
         14 . A system according to  claim 12 , wherein the training recommendation prompts are created for the evaluation results that comprise one or more performance indicators that are lower than the set thresholds for the one or more performance indicators. 
     
     
         15 . A system according to  claim 12 , wherein the creation of evaluation prompts comprises creating a vector store for the interaction data items. 
     
     
         16 . A system according to  claim 15 , wherein the generation of evaluation results for the evaluation prompts comprises conducting a similarity search using the created vector store. 
     
     
         17 . A system according to  claim 12 , wherein the training categories are separated into training data items that are stored in a vector store. 
     
     
         18 . A system according to  claim 17 , wherein the generation of training recommendations comprises conducting a similarity search using the created vector store. 
     
     
         19 . A system according to  claim 12 , wherein the machine learning comprises generative artificial intelligence. 
     
     
         20 . A method of rating agent performance in interactions and providing suggestions for improvement, the method comprising:
 creating one or more prompts for the evaluation of interaction data items in one or more interactions;   generating performance results for the interaction data items using the one or more prompts for the evaluation of interaction data items and machine learning;   creating coaching recommendation prompts for the performance results; and   generating coaching recommendations from coaching categories using the coaching recommendation prompts and machine learning.